Quantitative proteomics analysis of the effects of ionizing radiation in wild type and p53 K317R knock-in mouse thymocytes

Proteomic Changes in Mouse Spleen after Radiation-Induced Injury and its Modulation by Gamma-Tocotrienol

Gamma-tocotrienol (GT3), a naturally occurring vitamin E isomer, a promising radioprotector, has been shown to protect mice against radiation-induced hematopoietic and gastrointestinal injuries. We analyzed changes in protein expression profiles of spleen tissue after GT3 treatment in mice exposed to gamma radiation to gain insights into the molecular mechanism of radioprotective efficacy. Male CD2F1 mice, 12-to-14 weeks old, were treated with either vehicle or GT3 at 24 h prior to 7 Gy total-body irradiation. Nonirradiated vehicle, nonirradiated GT3 and age-matched naïve animals were used as controls. Blood and tissues were harvested on days 0, 1, 2, 4, 7, 10 and 14 postirradiation. High-resolution mass-spectrometry-based radioproteomics was used to identify differentially expressed proteins in spleen tissue with or without drug treatment. Subsequent bioinformatic analyses helped delineate molecular markers of biological pathways and networks regulating the cellular radiation responses in spleen. Our results show a robust alteration in spleen proteomic profiles including upregulation of the Wnt signaling pathway and actin-cytoskeleton linked proteins in mediating the radiation injury response in spleen. Furthermore, we show that 24 h pretreatment with GT3 attenuates radiation-induced hematopoietic injury in the spleen by modulating various cell signaling proteins. Taken together, our results show that the radioprotective effects of GT3 are mediated, via alleviation of radiation-induced alterations in biochemical pathways, with wide implications on overall hematopoietic injury.

p53-Regulated Networks of Protein, mRNA, miRNA, and lncRNA Expression Revealed by Integrated Pulsed Stable Isotope Labeling With Amino Acids in Cell Culture (pSILAC) and Next Generation Sequencing (NGS) Analyses

We determined the effect of p53 activation on de novo protein synthesis using quantitative proteomics (pulsed stable isotope labeling with amino acids in cell culture/pSILAC) in the colorectal cancer cell line SW480. This was combined with mRNA and noncoding RNA expression analyses by next generation sequencing (RNA-, miR-Seq). Furthermore, genome-wide DNA binding of p53 was analyzed by chromatin-immunoprecipitation (ChIP-Seq). Thereby, we identified differentially regulated proteins (542 up, 569 down), mRNAs (1258 up, 415 down), miRNAs (111 up, 95 down) and lncRNAs (270 up, 123 down). Changes in protein and mRNA expression levels showed a positive correlation (r = 0.50, p < 0.0001). In total, we detected 133 direct p53 target genes that were differentially expressed and displayed p53 occupancy in the vicinity of their promoter. More transcriptionally induced genes displayed occupied p53 binding sites (4.3% mRNAs, 7.2% miRNAs, 6.3% lncRNAs, 5.9% proteins) than repressed genes (2.4% mRNAs, 3.2% miRNAs, 0.8% lncRNAs, 1.9% proteins), suggesting indirect mechanisms of repression. Around 50% of the down-regulated proteins displayed seed-matching sequences of p53-induced miRNAs in the corresponding 3'-UTRs. Moreover, proteins repressed by p53 significantly overlapped with those previously shown to be repressed by miR-34a. We confirmed up-regulation of the novel direct p53 target genes LINC01021, MDFI, ST14 and miR-486 and showed that ectopic LINC01021 expression inhibits proliferation in SW480 cells. Furthermore, KLF12, HMGB1 and CIT mRNAs were confirmed as direct targets of the p53-induced miR-34a, miR-205 and miR-486-5p, respectively. In line with the loss of p53 function during tumor progression, elevated expression of KLF12, HMGB1 and CIT was detected in advanced stages of cancer. In conclusion, the integration of multiple omics methods allowed the comprehensive identification of direct and indirect effectors of p53 that provide new insights and leads into the mechanisms of p53-mediated tumor suppression.

Mutant TP53 posttranslational modifications: challenges and opportunities

The wild-type (WT) human p53 (TP53) tumor suppressor can be posttranslationally modified at over 60 of its 393 residues. These modifications contribute to changes in TP53 stability and in its activity as a transcription factor in response to a wide variety of intrinsic and extrinsic stresses in part through regulation of protein-protein and protein-DNA interactions. The TP53 gene frequently is mutated in cancers, and in contrast to most other tumor suppressors, the mutations are mostly missense often resulting in the accumulation of mutant (MUT) protein, which may have novel or altered functions. Most MUT TP53s can be posttranslationally modified at the same residues as in WT TP53. Strikingly, however, codons for modified residues are rarely mutated in human tumors, suggesting that TP53 modifications are not essential for tumor suppression activity. Nevertheless, these modifications might alter MUT TP53 activity and contribute to a gain-of-function leading to increased metastasis and tumor progression. Furthermore, many of the signal transduction pathways that result in TP53 modifications are altered or disrupted in cancers. Understanding the signaling pathways that result in TP53 modification and the functions of these modifications in both WT TP53 and its many MUT forms may contribute to more effective cancer therapies.

Applying mass spectrometry-based proteomics to genetics, genomics and network biology

The systematic and quantitative molecular analysis of mutant organisms that has been pioneered by studies on mutant metabolomes and transcriptomes holds great promise for improving our understanding of how phenotypes emerge. Unfortunately, owing to the limitations of classical biochemical analysis, proteins have previously been excluded from such studies. Here we review how technical advances in mass spectrometry-based proteomics can be applied to measure changes in protein abundance, posttranslational modifications and protein-protein interactions in mutants at the scale of the proteome. We finally discuss examples that integrate proteomics data with genomic and phenomic information to build network-centred models, which provide a promising route for understanding how phenotypes emerge.